Improving the recognition of grips and movements of the hand using myoelectric signals.

Journal: BMC medical informatics and decision making
Published Date: Jul 21, 2016

Abstract

BACKGROUND: People want to live independently, but too often disabilities or advanced age robs them of the ability to do the necessary activities of daily living (ADLs). Finding relationships between electromyograms measured in the arm and movements of the hand and wrist needed to perform ADLs can help address performance deficits and be exploited in designing myoelectrical control systems for prosthetics and computer interfaces.

Authors

  • Gene Shuman
    Department of Computer Science, Volgenau School of Engineering, George Mason University, 4400 University Drive, Fairfax, 22030, VA, USA. gshuman@gmu.edu.
  • Zoran Durić
    Department of Computer Science, Volgenau School of Engineering, George Mason University, 4400 University Drive, Fairfax, 22030, VA, USA.
  • Daniel Barbará
    Department of Computer Science, Volgenau School of Engineering, George Mason University, 4400 University Drive, Fairfax, 22030, VA, USA.
  • Jessica Lin
    Department of Computer Science, Volgenau School of Engineering, George Mason University, 4400 University Drive, Fairfax, 22030, VA, USA.
  • Lynn H Gerber
    Center for the Study of Chronic Illness and Disability, George Mason University, 4400 University Drive, Fairfax, 22030, VA, USA.

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